According to one embodiment, a data analyzing apparatus acquires data containing the number N of analysis target samples (where N is an integer larger than or equal to 2). The apparatus performs a matrix factorization upon the data to factorize the data into the number K of basis samples and the number K of weights corresponding to the number K of basis samples (where K is an integer larger than or equal to 2), and fixes part of the K basis samples to specific basis samples in the matrix factorization.

A measurement apparatus comprising an acquisition memory adapted to store data sections of at least one acquired measurement signal; a processor adapted to calculate a measurement parameter vector, v, for each data section of the acquired measurement signal; and a trained autoencoder neural network adapted to process the measurement parameter vectors, v, applied as input data to the trained autoencoder neural network to provide at a middle layer of said autoencoder neural network an encoded vector, h, with characteristic signal features of the acquired measurement signal.

Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

A method of identifying malicious activity in a computer data sequence includes providing provided the computer data sequence to a network configured to convert the computer data sequence from a high-dimensional space to a low-dimensional space, and processing the computer data sequence in the low-dimensional space to generate an approximately Gaussian distribution. The processed computer data sequence converted to the low dimensional space is evaluated relative to the approximately Gaussian distribution to determine whether the computer data sequence is likely malicious or likely benign, and an output is provided indicating whether the computer data sequence is likely malicious or likely benign.

Methods and servers for storing data associated with users and digital items of a recommendation system having access to non-distributed and distributed storages. The server trains a model based for generating first user and item embeddings. The server stores (i) the first user embeddings in the non-distributed storage, and (ii) the first item embeddings in the distributed storage. The server re-trains the model for generating second user and item embeddings. The server stores (i) the second user embeddings in the non-distributed storage in addition to the first user embeddings, and (ii) second item embeddings in the distributed storage instead of the respective first item embeddings by replacing the respective first item embeddings. When the second item embeddings are stored on each node of the distributed storage, the server removes the first user embeddings associated with the first value from the non-distributed storage.

A system is in communication with a managed infrastructure. An extraction engine is in communication with a managed infrastructure. The extraction engine is configured to receive managed infrastructure data and produces events as well as populates an entropy database with a dictionary of event entropy that can be included in the entropy database. A signalizer engine that includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The signalizer engine inputs a list of devices and a list of connections between components or nodes in the managed infrastructure. The signalizer engine determines one or more common characteristics and produces clusters of events relating to failure or errors in at least one of the devices and connections between components or nodes in the managed infrastructure. The events are converted into words and subsets to group the events into clusters that relate to security of the managed infrastructure. In response to grouping the events, physical changes are made to at least a portion of the physical hardware. In response to production of the clusters, security of the managed infrastructure is maintained.

Low-dimensional feature values with which semantic factors of content are ascertained are generated from relevance between sets of two types of content.

Based on a relation indicator indicating a pair of groups indicating which groups are related to first types of content groups among second types of content groups, an initial feature value extracting unit 11 extracts initial feature values of the first type of content and the second type of content. A content pair selecting unit 12 selects a content pair by selecting one first type of content and one second type of content from each pair of groups indicated by the relation indicator. A feature value conversion function generating unit 13 generates feature conversion functions 31 of converting the initial feature values into low-dimensional feature values based on the content pair selected from each pair of groups.

A computer-implemented method is provided for evaluating a pilot tone signal. In the method, the pilot tone signal is recorded using a high-frequency coil arrangement of a magnetic resonance facility and describes a movement of a patient. The method also includes extracting movement information assigned to a movement component, (e.g., a respiratory movement). A breakdown or decomposition of the pilot tone signal is effected on a basis of signal components having assigned weightings and for the purpose of determining the movement information, a part of a base which is assigned to the movement component is selected by a selection criterion. For the purpose of determining the base and the weightings, a non-negative matrix factorization is performed, in the context of which a signal matrix, which is formed from the pilot tone signal and is in particular non-negative, is formulated as a product of a non-negative signal component matrix that describes the base and a non-negative weighting matrix that describes the weightings.

Provided is a method of predicting a condition of a patient, including decomposing an input matrix to produce a residual matrix, the input matrix representing patients and expression levels of genes of the patients, training a classifier by using health condition values the patients as learning criteria and inputting the residual matrix into the classifier, and obtaining a value for predicting a health condition of a first patient, by inputting a first input matrix including the expression levels of genes of the first patient into the classifier, the first residual matrix being a residual matrix obtained from the first input matrix by using the predetermined algorithm.

Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.